Enhancing Efficiency and Workflow of Bio Statistics Teams

Converting Repeated Processes to a Shiny Suite of Applications for
Reproducibility, Reporting, and Scalability

Mayank Agrawal
Senior R developer Consultant I
ProCogia

Friday, October 27, 2023

Phase 2 Trails

A Phase 2 trial answers the question, “Does Drug X improve Disease Y?”

  • Phase 2 clinical trials assess the safety and efficacy of a new drug or drug combination for a specific medical condition.
  • Goal: Determine appropriate dose and treatment plan for Phase 3 testing.
  • Phase 2a: Involves fewer patients, generally 100-300 patients to focus on dose-response relationships and optimal dosing frequency.
  • Phase 2b: Rigorously assesses drug’s effectiveness in disease treatment, prevention, or diagnosis.
  • Assess therapeutic effectiveness in a specific patient group for potential Phase 3 study.
  • Also used to assess and review safety parameters for potential adverse events that might have been missed in a particular patient group.

Challenges

Dose Simulations for Phase 2 trails are often complex and time consuming with repeated similar workflow steps for each new variation of the dosage trail.

This often leads to

  • Delayed Analysis and reporting
  • Longer study time (years)
  • Delayed Time to Market

resulting in

  • Hindered workflows
  • Lower productivity
  • Repeated boring processes

Key Issues

  • Manual processes
  • Lengthy simulation times
  • Scalability constraints
  • Limited collaboration
  • Reporting challenges
  • Flexibility
  • Reproducibility

Meeting the Challenges

Empowering Biostatisticians with discipline and the right set of tools.

  • Collaborated with Biostats to understand their pain points and challenges.
  • An operational framework rooted in Agile development methodology, with a focus on empowering Biostats at its core.
  • Enhanced workflow efficiency, reproducibility and productivity among the biostats team.
    • Reproducible workflows with RStudio Projects, Git, R, Shiny, renv and CRAN packages.
    • Scalable applications built with Software Engineering principles.
    • Improved automated interactive reporting.
    • Automated testing framework for validated analysis results.
  • The framework operated like a well-oiled machine, effectively engaging Biostats with discipline and empowering them with new tools in the expanding ecosystem.

Getting Started

  • Define scope of each process.
  • Document repetition rate, importance, and time investment for each work request.
  • Identify the most time-consuming, yet simplest workflow.
  • Develop a MVP (Minimum Viable Product)
    • Showcase a demo with the smallest workflow.
    • This aids leaders in visualizing the impact of approval.
  • Integrate workflows incrementally from small to large.
  • Continuously improve process through rapid iterations, responding to user feedback, ideas and feature requests.

Framework Principles

  • Showcase early application design outlines using draw.io for UI layout prototyping.
  • Invest time to establish a standard application template layout for the ecosystem with the organizational color scheme.
  • Create smaller, independent Proof of Concepts (POCs) for new feature requests.
  • Define the flow of reactivity for the overall application.
  • Prioritize user-friendliness: if it’s not intuitive, it won’t be used.
  • Prioritize user-requests based on need, impact, time, effort and complexity.
  • Enable consistent reporting with parameterized Markdown/Quarto for dynamic MS Word reports, following organizational templates.
  • Streamline workflow by adopting standard coding principles and agile project management.

Tools Selection

  • Shiny for building interactive web apps straight from R.
    • Enhanced collaboration with Biostats in R; their preferred language for analysis and visualization of complex clinical trial data.
    • Seamless integration of Tables, Listings, and Graphs (TLGs).
    • Enabled real-time updates with quick release cycles, crucial for adapting to evolving trial needs.
    • Enhanced decision-making thru interactive tools.
  • renv as a package management tool to ensure reproducibility across the team and environments.
    • CRAN published and maintained packages to ensure accurate and consistent results.

Enhancing Scalability

  • Implement modularization and functional programming for a plug-and-play development format across applications.
  • Enable multiple studies to be added concurrently using standard git branching strategies, involving multiple concurrent developers.
  • Async Programming: Evaluate longer simulations in a separate R process preventing app performance issues.
  • Take a step further and deploy simulation functions as internal APIs with Plumber.
  • Write your custom JavaScript and R bindings for implementing unique feature requests.
  • Approach feature requests as a blend of web development, software engineering, and R development.

Ensuring Reproducibility with Automated Testing

  • Writing Test Cases (Inputs, Expected Outputs)
  • Full Stack Testing
  • Types of Tests: Unit, Functional, Integration, and End-to-End
  • Continuous Integration for Testing with Git branching strategies.
  • Benefits
    • Early bug detection
    • Efficiency and speed
    • Consistent and repeatable testing
    • Increased test coverage
    • Regression testing capabilities
    • Greater confidence in release stability

Enhancing Adoption

  • Invest time to create an in-depth GitHub ReadMe providing comprehensive project reproducibility instructions.
  • Include application workflow GIFs in announcement emails.
  • Create detailed application interaction [user manuals{style=“text-decoration:underline”} with screenshots and highlights for each step.
  • Conduct regular (quarterly) training sessions to provide guidance, answer questions, and assist users with new features.
    • Record and share them for easier re-visit.
  • Continuously engage user base for better ROI and on boarding.
  • Prioritize most requested user features for each sprint.
  • Have a team of Application Champions available for queries/requests.

Thank you

References - R Packages

R Pharma 2023